The introduction of connected autonomous vehicles (CAVs) potentially improves the link capacity and backward wave speed of traffic flow, while the advanced communication technology could well make it possible to allow CAV users to share their travel information. To bridge the knowledge gaps in the network evolution under mixed environment of human-driven vehicles (HVs) and CAVs, it is essential to explore multi-dimensional dynamic traffic assignment. An inertia-based multi-class dual dynamic traffic assignment (IMDDTA) model is proposed to capture the intraday and diurnal variations of the mixed traffic flow under the disequilibrium state simultaneously. Specifically, in this study we consider the inertia of HV users as well the information-sharing behaviour of CAV users respectively, characterized by different extensions of the multinomial logit (MNL) model. To demonstrate the properties of the model, two numerical case studies are conducted based on the Braess network and the Sioux Falls network. The results indicate an acceptable validity and applicability of the model and provide valuable insights on the evolution of traffic flow under mixed environment.

中文翻译：

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利用人类驾驶车辆和联网自动驾驶车辆之间的信息感知差异对双动态交通流演化进行建模

联网自动驾驶汽车 (CAV) 的引入有望提高交通流的链路容量和后向波速，而先进的通信技术很可能使 CAV 用户共享其出行信息成为可能。为了弥合人类驾驶车辆（HV）和CAV混合环境下网络演进的知识差距，有必要探索多维动态交通分配。提出了一种基于惯性的多类双动态交通分配（IMDDTA）模型，以同时捕获不平衡状态下混合交通流的日内和日内变化。具体来说，在本研究中，我们分别考虑了 HV 用户的惯性以及 CAV 用户的信息共享行为，其特征是多项 Logit（MNL）模型的不同扩展。为了证明该模型的特点，基于 Braess 网络和 Sioux Falls 网络进行了两个数值案例研究。结果表明该模型具有可接受的有效性和适用性，并为混合环境下交通流的演变提供了有价值的见解。